Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Causal Bandits: Learning Good Interventions via Causal Inference
Authors: Finnian Lattimore, Tor Lattimore, Mark D. Reid
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We compare Algorithms 1 and 2 with the Successive Reject algorithm of Audibert and Bubeck (2010), Thompson Sampling and UCB under a variety of conditions. ... For each experiment, we show the average regret over 10,000 simulations with error bars displaying three standard errors. |
| Researcher Affiliation | Collaboration | Finnian Lattimore Australian National University and Data61/NICTA EMAIL Tor Lattimore Indiana University, Bloomington EMAIL Mark D. Reid Australian National University and Data61/NICTA EMAIL |
| Pseudocode | Yes | Algorithm 1 Parallel Bandit Algorithm |
| Open Source Code | Yes | The code is available from <https://github.com/finnhacks42/causal_bandits> |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year) for a publicly available or open dataset was found. The paper describes using a synthetic model for its experiments: 'Throughout we use a model in which Y depends only on a single variable X1 (this is unknown to the algorithms). Yt Bernoulli( 1 2 + ε) if X1 = 1 and Yt Bernoulli( 1 2 ε ) otherwise, where ε = q1ε/(1 q1).' |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, citations to predefined splits) was provided. The paper describes a sequential decision problem where data is collected iteratively over 'T rounds' rather than using pre-defined dataset splits. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were mentioned. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) were mentioned. |
| Experiment Setup | Yes | For the first T/2 rounds it chooses do() to collect observational data. ... In Figure 2a we fix the number of variables N and the horizon T and compare the performance of the algorithms as m increases. ... Throughout we use a model in which Y depends only on a single variable X1 (this is unknown to the algorithms). Yt Bernoulli( 1 2 + ε) if X1 = 1 and Yt Bernoulli( 1 2 ε ) otherwise, where ε = q1ε/(1 q1). ... Input: Total rounds T and N. (Algorithm 1) ... Input: T, η [0, 1]A, B [0, )A (Algorithm 2) |